Performance Characterization and Efficient Parallelization of MASNUM Wave Model

Marine science and numerical modeling (MASNUM) is a numerical wave model developed by China, which has been widely used in wave forecasting for ocean disaster prevention and reduction, ocean transportation and military activities. With the increasing demands on higher forecasting precision and climate research, higher and higher resolution becomes a main stream in wave model development. Although the fast development of high-performance computer provides increasing computing power for high-resolution model, parallel version of model is always inefficient to achieve sufficient performance acceleration that can improve the parallel efficiency of the wave model and can shorten the running wall time. In this paper, we firstly characterize the performance of the MASNUM model on a modern high-performance computer to reveal several performance bottlenecks. Then, we propose several parallel optimizations, which dramatically improve communication performance, I/O performance and load balance of two dimension parallel decomposition. And these parallel optimizations consequently significantly improve the overall parallel efficiency and scaling performance of MASNUM model. When we use 960 CPU cores in order to check the MASNUM performance acceleration, the improved parallel version can achieve 4315-fold speedup with the baseline of sequential performance. Based on our experiments, we suggest setting some parallel efficient strategies in order to achieve the high parallel efficiency of other numerical models.